1. Semantics and analytics = making the data and the decisions smarter? Digital Antiquity CI Feb 7-8, 2013, Arlington VA Peter Fox (RPI and WHOI) pfox@cs.rpi.edu, @taswegian,pfox@cs.rpi.edu http://tw.rpi.edu/web/person/PeterFox Tetherless World Constellation http://tw.rpi.edu and AOP&Ehttp://tw.rpi.edu

2. Analytics – data and visual

4. 4 DataInformationKnowledge ProducersConsumers Context Presentation Organization Integration Conversation Creation Gathering Experience Analytics Ecosystem Stimulate Innovation Research Exploration Discovery

5. Data as Infostructure

6. Curation for analytics 6 ProducersConsumers Quality Control Fitness for Purpose Fitness for Use Quality Assessment Trustee Trustor Others…

7. Technical advances From: C. Borgman, 2008, NSF Cyberlearning Report

8. Working with knowledge Expressivity Maintainability/ Extensibility Implement -ability Query Rule execution Inference

9. For real discovery – we need abduction! - a method of logical inference introduced by C. S. Peirce which comes prior to induction and deduction for which the colloquial name is to have a "hunch” Importantly - human intuition is needed in interacting with large-scale data

10. Yes, we need a Knowledge Base Knowledge provenance Descriptions of the artifacts Domain specific terms/ language 10 Questions Who What/when/why/ how Answer

11. Smart visual exploration

12. Semantics - Modern informatics enables a new scale-free** framework approach Use cases Stakeholders Distributed authority Access control Ontologies Maintaining Identity

13. Finally Significant opportunities for smart data-as-a-service approaches to ‘scale’ for big data (on the web) Delivering ‘products’ allows analytics on the back end, but tools to plug into a framework are lacking Exploit late semantic binding for ABDUCTION Next generation analytics must accommodate: abduction, translucency, interactivity and retain what they do well! So we all need to get cracking! Thanks. @taswegian, pfox@cs.rpi.edupfox@cs.rpi.edu

14. Back shed

15. Fox & McGuinness Semantic Technologies May 21, 2007 1: Integrating Multiple Data Sources The Semantic Web lets us merge statements from different sources The RDF Graph Model allows programs to use data uniformly regardless of the source Figuring out where to find such data is a motivator for Semantic Web Services #Ionosphere#magnetic “100” “Terrestrial Ionosphere” name hasCoordinates hasLowerBoundaryValue Different line & text colors represent different data sources hasLowerBoundaryUnit “km”

16. Fox & McGuinness Semantic Technologies May 21, 2007 2: Drill Down /Focused Perusal The Semantic Web uses Uniform Resource Identifiers (URIs) to name things These can typically be resolved to get more information about the resource This essentially creates a web of data analogous to the web of text created by the World Wide Web Ontologies are represented using the same structure as content –We can resolve class and property URIs to learn about the ontology Internet …#NeutralTemperature...#ISR …#Norway …#EISCAT measuredby type locatedIn...#FPI...#MilllstoneHill operatedby

17. Fox & McGuinness Semantic Technologies May 21, 2007 3: Statements about Statements The Semantic Web allows us to make statements about statements –Timestamps –Provenance / Lineage –Authoritativeness / Probability / Uncertainty –Security classification –… This is an unsung virtue of the Semantic Web #Aurora Red #Danny’s 20031031 hascolor hasSource hasDateTime Ontologies Workshop, APL May 26, 2006

18. Fox & McGuinness Semantic Technologies May 21, 2007 8: Proof The logical foundations of the Semantic Web allow us to construct proofs that can be used to improve transparency, understanding, and trust Proof and Trust are on- going research areas for the Semantic Web #FlatField #Critical Dataset #Solar Physics Paper hasCalibration hasPeerReview “Critical Dataset has been calibrated with a flat field program that is published In the peer reviewed literature.”

19. 19 Knowledge representation Statements as triples: {subject-predicate-object} interferometer is-a optical instrument Fabry-Perot is-a interferometer Optical instrument has focal length Optical instrument is-a instrument Instrument has instrument operating mode Instrument has measured parameter Instrument operating mode has measured parameter NeutralTemperature is-a temperature Temperature is-a parameter A query*: select all optical instruments which have operating mode vertical An inference: infer operating modes for a Fabry-Perot Interferometer which measures neutral temperature ISWC paper award 2006, IAAI best paper (2007), Fox et al. 2009 in Computers and Geosciences.

20. Visual discovery

21. Traversal for new patterns

22. However - Skill/ tools?

23. Summary Get the data well structured! Be aware of the distinctions between data, information, knowledge. Develop multi-domain KBs Use the standards, and tools that are available Get familiar with semantic technology but do not let it drive what you explore

24. And… Frameworks more than systems Leverage semantic methodologies that are shown to work/ be useful Vocabulary development … by communities, leverage what you have and for the things that matter Exploit late semantic binding for ABDUCTION